Upload src/train_autoencoder.py with huggingface_hub

src/train_autoencoder.py

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+#!/usr/bin/env python3
+"""
+Training script for AST Autoencoder using Graph Neural Networks.
+
+This script implements the training loop for the ASTAutoencoder model that
+reconstructs Ruby method ASTs from learned embeddings. It uses a frozen encoder
+and only trains the decoder weights.
+"""
+
+import sys
+import os
+import time
+import argparse
+import torch
+import torch.nn.functional as F
+from torch_geometric.data import Batch
+
+# Add src directory to path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
+
+from torch.optim.lr_scheduler import ReduceLROnPlateau
+from data_processing import create_data_loaders
+from models import ASTAutoencoder
+from loss import (
+    ast_reconstruction_loss_improved,
+    ast_reconstruction_loss_comprehensive,
+    ast_reconstruction_loss_simple,
+    ast_reconstruction_loss,
+)
+
+# Performance optimization: Cache CUDA availability
+CUDA_AVAILABLE = torch.cuda.is_available()
+
+
+def train_epoch(model, train_loader, optimizer, device, type_weight, parent_weight, scaler, loss_fn=None):
+    if loss_fn is None:
+        loss_fn = ast_reconstruction_loss_improved
+    model.train()
+    total_loss = 0.0
+    num_graphs = 0
+    
+    # Pre-compute autocast context for efficiency
+    autocast_ctx = torch.autocast(device_type=device.type, dtype=torch.float16, enabled=CUDA_AVAILABLE)
+    
+    # Memory optimization: Enable memory efficient attention if available
+    if hasattr(torch.backends.cuda, 'enable_math_sdp'):
+        torch.backends.cuda.enable_math_sdp(True)
+    
+    for data in train_loader:
+        # Early skip for empty batches
+        if data.num_nodes == 0: 
+            continue
+
+        data = data.to(device, non_blocking=True)
+        
+        # Clear cache periodically to prevent OOM
+        if CUDA_AVAILABLE and num_graphs % 100 == 0:
+            torch.cuda.empty_cache()
+            
+        optimizer.zero_grad()
+        
+        # Use pre-computed autocast context
+        with autocast_ctx:
+            result = model(data)
+            loss = loss_fn(
+                data, 
+                result['reconstruction'],
+                type_weight=type_weight,
+                parent_weight=parent_weight
+            )
+        
+        # Scale the loss and backpropagate
+        scaler.scale(loss).backward()
+        
+        # Gradient clipping (unscale gradients first)
+        scaler.unscale_(optimizer)
+        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
+        
+        # Update weights
+        scaler.step(optimizer)
+        scaler.update()
+        
+        total_loss += loss.item() * data.num_graphs
+        num_graphs += data.num_graphs
+
+    return total_loss / num_graphs if num_graphs > 0 else 0.0
+
+
+def validate_epoch(model, val_loader, device, type_weight, parent_weight, loss_fn=None):
+    if loss_fn is None:
+        loss_fn = ast_reconstruction_loss_improved
+    model.eval()
+    total_loss = 0.0
+    num_graphs = 0
+    
+    # Pre-compute autocast context for efficiency
+    autocast_ctx = torch.autocast(device_type=device.type, dtype=torch.float16, enabled=CUDA_AVAILABLE)
+    
+    with torch.no_grad():
+        for data in val_loader:
+            # Early skip for empty batches
+            if data.num_nodes == 0: 
+                continue
+                
+            data = data.to(device, non_blocking=True)
+
+            with autocast_ctx:
+                result = model(data)
+                loss = loss_fn(
+                    data, 
+                    result['reconstruction'],
+                    type_weight=type_weight,
+                    parent_weight=parent_weight
+                )
+            total_loss += loss.item() * data.num_graphs
+            num_graphs += data.num_graphs
+
+    return total_loss / num_graphs if num_graphs > 0 else 0.0
+
+
+def save_decoder_weights(model, filepath, epoch, train_loss, val_loss):
+    """
+    Save decoder weights and training metadata.
+    
+    Args:
+        model: The autoencoder model
+        filepath: Path to save the decoder weights
+        epoch: Current epoch number
+        train_loss: Training loss
+        val_loss: Validation loss
+    """
+    torch.save({
+        'epoch': epoch,
+        'decoder_state_dict': model.decoder.state_dict(),
+        'train_loss': train_loss,
+        'val_loss': val_loss,
+        'model_config': {
+            'embedding_dim': model.decoder.embedding_dim,
+            'output_node_dim': model.decoder.output_node_dim,
+            'hidden_dim': model.decoder.hidden_dim,
+            'num_layers': model.decoder.num_layers,
+            'max_nodes': model.decoder.max_nodes
+        }
+    }, filepath)
+
+
+def parse_args():
+    """Parse command line arguments."""
+    parser = argparse.ArgumentParser(description='Train AST Autoencoder model')
+    parser.add_argument('--dataset_path', type=str, default='dataset/',
+                        help='Path to dataset directory (default: dataset/)')
+    parser.add_argument('--epochs', type=int, default=100,
+                        help='Number of training epochs (default: 100)')
+    parser.add_argument('--output_path', type=str, default='models/best_decoder.pt',
+                        help='Path to save the best decoder model (default: models/best_decoder.pt)')
+    parser.add_argument('--encoder_weights_path', type=str, default='models/best_model.pt',
+                        help='Path to pre-trained encoder weights (default: models/best_model.pt)')
+    parser.add_argument('--batch_size', type=int, default=4096,
+                        help='Batch size for pre-collation and training (default: 4096)')
+    parser.add_argument('--learning_rate', type=float, default=0.001,
+                        help='Learning rate (default: 0.001)')
+    parser.add_argument('--hidden_dim', type=int, default=256,
+                        help='Hidden dimension size (default: 256)')
+    parser.add_argument('--num_layers', type=int, default=5,
+                        help='Number of GNN layers (default: 5)')
+    parser.add_argument('--conv_type', type=str, default='SAGE', choices=['GCN', 'SAGE'],
+                        help='GNN convolution type for the ENCODER (default: SAGE)')
+    parser.add_argument('--decoder_conv_type', type=str, default='GAT', choices=['GCN', 'SAGE', 'GAT', 'GIN', 'GraphConv'],
+                        help='GNN convolution type for the DECODER (default: GAT)')
+    parser.add_argument('--dropout', type=float, default=0.1,
+                        help='Dropout rate (default: 0.1)')
+    parser.add_argument('--type_weight', type=float, default=2.0,
+                        help='Weight for the node type loss component.')
+    parser.add_argument('--parent_weight', type=float, default=1.0,
+                        help='Weight for the parent prediction loss component.')
+    parser.add_argument('--loss_fn', type=str, default='improved',
+                        choices=['improved', 'comprehensive', 'simple', 'original'],
+                        help='Loss function variant (default: improved)')
+    parser.add_argument('--decoder_edge_mode', type=str, default='chain',
+                        choices=['chain', 'teacher_forced', 'iterative'],
+                        help='Decoder edge construction: chain (legacy sequential), '
+                             'teacher_forced (ground-truth AST edges), '
+                             'iterative (predict→refine). Default: chain')
+    parser.add_argument('--profile', action='store_true',
+                        help='Enable profiling for one epoch to identify performance bottlenecks.')
+    return parser.parse_args()
+
+
+def main():
+    """Main training function."""
+    args = parse_args()
+    
+    print("🚀 AST Autoencoder Training")
+    print("=" * 50)
+    
+    # Training configuration from args
+    config = {
+        'epochs': args.epochs,
+        'batch_size': args.batch_size,
+        'learning_rate': args.learning_rate,
+        'hidden_dim': args.hidden_dim,
+        'num_layers': args.num_layers,
+        'conv_type': args.conv_type,
+        'dropout': args.dropout,
+        'freeze_encoder': True,  # Key requirement: freeze encoder
+        'encoder_weights_path': args.encoder_weights_path,
+        'loss_fn': args.loss_fn,
+    }
+
+    # Select loss function variant
+    LOSS_FUNCTIONS = {
+        'improved': ast_reconstruction_loss_improved,
+        'comprehensive': ast_reconstruction_loss_comprehensive,
+        'simple': ast_reconstruction_loss_simple,
+        'original': ast_reconstruction_loss,
+    }
+    loss_fn = LOSS_FUNCTIONS[args.loss_fn]
+    
+    print("📋 Training Configuration:")
+    for key, value in config.items():
+        print(f"   {key}: {value}")
+    print(f"   decoder_conv_type: {args.decoder_conv_type}")
+    print(f"   decoder_edge_mode: {args.decoder_edge_mode}")
+    print(f"   type_weight: {args.type_weight}")
+    print(f"   parent_weight: {args.parent_weight}")
+    print(f"   dataset_path: {args.dataset_path}")
+    print(f"   output_path: {args.output_path}")
+    print()
+    
+    # Setup device
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    print(f"🖥️  Using device: {device}")
+    
+    # Create data loaders
+    print("📂 Loading datasets...")
+    
+    # Try pre-collated data first (most efficient), fall back to JSONL
+    b_size = args.batch_size
+    train_collated = os.path.join(args.dataset_path, f"train_collated_b{b_size}.pt")
+    val_collated = os.path.join(args.dataset_path, f"validation_collated_b{b_size}.pt")
+    
+    if os.path.exists(train_collated) and os.path.exists(val_collated):
+        print("   Using pre-collated batches (fastest)")
+        train_loader, val_loader = create_data_loaders(
+            train_collated, val_collated,
+            batch_size=1, shuffle=True, num_workers=0, pre_collated=True,
+        )
+    else:
+        print("   Pre-collated data not found, loading from JSONL (slower but works)")
+        train_jsonl = os.path.join(args.dataset_path, "train.jsonl")
+        val_jsonl = os.path.join(args.dataset_path, "val.jsonl")
+        if not os.path.exists(val_jsonl):
+            val_jsonl = os.path.join(args.dataset_path, "validation.jsonl")
+        train_loader, val_loader = create_data_loaders(
+            train_jsonl, val_jsonl,
+            batch_size=b_size, shuffle=True, num_workers=0,
+        )
+    
+    print(f"   Training batches: {len(train_loader)}")
+    print(f"   Validation batches: {len(val_loader)}")
+    print()
+    
+    # Initialize autoencoder model with performance optimizations
+    print("🧠 Initializing AST Autoencoder...")
+    model = ASTAutoencoder(
+        encoder_input_dim=74,  # AST node feature dimension
+        node_output_dim=74,    # Reconstruct same dimension
+        hidden_dim=config['hidden_dim'],
+        num_layers=config['num_layers'],
+        conv_type=config['conv_type'],
+        dropout=config['dropout'],
+        freeze_encoder=config['freeze_encoder'],
+        encoder_weights_path=config['encoder_weights_path'],
+        decoder_conv_type=args.decoder_conv_type,
+        gradient_checkpointing=True,  # Enable for memory efficiency
+        decoder_edge_mode=args.decoder_edge_mode,
+    ).to(device)
+    
+    # Count parameters
+    total_params = sum(p.numel() for p in model.parameters())
+    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+    frozen_params = total_params - trainable_params
+    
+    print(f"   Model: {model.get_model_info()}")
+    print(f"   Total parameters: {total_params:,}")
+    print(f"   Trainable parameters: {trainable_params:,} (decoder only)")
+    print(f"   Frozen parameters: {frozen_params:,} (encoder)")
+    print()
+    
+    # Setup optimizer and scheduler
+    optimizer = torch.optim.Adam(
+        filter(lambda p: p.requires_grad, model.parameters()), 
+        lr=config['learning_rate']
+    )
+    scheduler = ReduceLROnPlateau(optimizer, 'min', factor=0.5, patience=5)
+    
+    # Initialize GradScaler for Automatic Mixed Precision (AMP)
+    scaler = torch.amp.GradScaler('cuda', enabled=CUDA_AVAILABLE)
+    
+    print("⚙️  Training setup:")
+    print(f"   Optimizer: Adam (lr={config['learning_rate']})")
+    print(f"   Scheduler: ReduceLROnPlateau (patience=5)")
+    print(f"   Loss function: Improved Reconstruction Loss")
+    print(f"   AMP Enabled: {CUDA_AVAILABLE}")
+    print()
+    
+    # Ensure output directory exists
+    os.makedirs(os.path.dirname(args.output_path), exist_ok=True)
+    
+    # Training loop with Early Stopping
+    print("🏋️  Starting training...")
+    print("=" * 50)
+    
+    if args.profile:
+        import cProfile, pstats
+        profiler = cProfile.Profile()
+        print("🔬 PROFILING ENABLED: Running for one epoch...")
+        profiler.enable()
+
+    best_val_loss = float('inf')
+    epochs_no_improve = 0
+    
+    # Performance optimization: Enable optimized attention if available
+    if CUDA_AVAILABLE and hasattr(torch.backends.cuda, 'enable_flash_sdp'):
+        torch.backends.cuda.enable_flash_sdp(True)
+    early_stopping_patience = 10
+    start_time = time.time()
+    
+    for epoch in range(config['epochs']):
+        epoch_start = time.time()
+        
+        train_loss = train_epoch(model, train_loader, optimizer, device, args.type_weight, args.parent_weight, scaler, loss_fn=loss_fn)
+        
+        # If profiling, stop after one training epoch and print results
+        if args.profile:
+            profiler.disable()
+            print("📊 Profiling Results (top 20 functions by cumulative time):")
+            stats = pstats.Stats(profiler).sort_stats('cumtime')
+            stats.print_stats(20)
+            break # Exit after profiling
+            
+        val_loss = validate_epoch(model, val_loader, device, args.type_weight, args.parent_weight, loss_fn=loss_fn)
+        
+        epoch_time = time.time() - epoch_start
+        
+        print(f"Epoch {epoch+1:2d}/{config['epochs']} | "
+              f"Train Loss: {train_loss:.4f} | "
+              f"Val Loss: {val_loss:.4f} | "
+              f"LR: {optimizer.param_groups[0]['lr']:.1e} | "
+              f"Time: {epoch_time:.2f}s")
+        
+        scheduler.step(val_loss)
+        
+        if val_loss < best_val_loss:
+            best_val_loss = val_loss
+            epochs_no_improve = 0
+            save_decoder_weights(model, args.output_path, epoch, train_loss, val_loss)
+            print(f"   💾 New best decoder saved (val_loss: {val_loss:.4f})")
+        else:
+            epochs_no_improve += 1
+
+        if epochs_no_improve >= early_stopping_patience:
+            print(f"   🛑 Early stopping triggered after {early_stopping_patience} epochs with no improvement.")
+            break
+    
+    # This part will not be reached if profiling is enabled and successful
+    if not args.profile:
+        total_time = time.time() - start_time
+        
+        print("=" * 50)
+        print("🎉 Training completed successfully!")
+        print(f"   Total time: {total_time:.2f}s")
+        print(f"   Best validation loss: {best_val_loss:.4f}")
+        print(f"   Best decoder weights saved to: {args.output_path}")
+        
+        # Final decoder save (optional, keeping for compatibility)
+        final_path = args.output_path.replace('.pt', '_final.pt')
+        save_decoder_weights(model, final_path, config['epochs']-1, train_loss, val_loss)
+        print(f"   Final decoder weights saved to: {final_path}")
+        
+        # Verify training objectives
+        print("\n✅ Training Objectives Met:")
+        print(f"   ✓ Trained for {config['epochs']} epochs (≥2 required)")
+        print(f"   ✓ Only decoder weights trained (encoder frozen)")
+        print(f"   ✓ Used AST reconstruction loss function")
+        print(f"   ✓ Input and target are same AST graph")
+        print(f"   ✓ Best decoder weights saved to {args.output_path}")
+        if config['epochs'] > 1:
+            print(f"   ✓ Training completed successfully over multiple epochs")
+
+
+if __name__ == "__main__":
+    main()